People can see things using binocular disparity in stereovision. The use of a stereo vision camera, which calculates distance information with two cameras according to such a principle, is a growing trend. The stereo vision camera is applied to cars (to guide a car when a car enters a garage, parks, or drives on a congested roadway or an express highway, or to easily drive a car on a surface street), mobile objects other than a car, robots, Factory Automation (FA), Laboratory Automation (LA), Office Automation (OA), Building Automation (BA), Home Automation (HA), precision measurement, etc. Generally, a stereo matching scheme is used to check distance information of an image using left and right stereo vision cameras.
That is, the stereo matching scheme is that a difference between two positions, i.e., disparity is extracted by detecting which position of other side image a pattern in a specific position of one side image is in using a binocular disparity characteristic that images are differently taken by two or more cameras are set up separated from each other by a certain distance, and thus a camera directly operates a distance value up to a real position of the pattern.
However, in the performance of the operation, the degrees of total images input from left and right cameras may differ according to the incident position of the characteristic light of each camera because of using the color information (a brightness value, etc) of left and right images, and calculation is performed for reducing an amount of calculation on the assumption that left lines respectively accord with right lines. Therefore, a good result cannot be obtained because there is a state where alignment is awry.
Accordingly, most of stereo vision systems solve the limitation by a hardware device capable of controlling the brightness value or alignment of left and right cameras or a software scheme. However, the hardware device has cases where a user directly controls the brightness value or alignment of the left and right cameras according to peripheral environments, and has the following limitations in a case where it is automated in a software manner. For example, in a software process, a board having a predetermined shape is photographed from various angles to be reflected. Referring to FIG. 1, each of the apexes and interconnection lines of a rectangle board having a chess-board shape is extracted and the alignment of left and right images is achieved using the extracted apexes and lines. In this case, although rectification is very accurately achieved, many images must be captured for the increment of accuracy, and apexes which are not automatically searched must directly be input by a user.
Moreover, since a stereo camera is not perfectly fixed, an alignment state again becomes awry due to a slight torsion, etc under use.
When the auto-exposure of the cameras is operated for use under the sensor difference of the each stereo camera or various lighting environments, the values of left and right cameras may become different. Because most of stereo vision systems calculate a depth map using a brightness value or a RGB value instead of a pattern of images, when the values of the left and right cameras are input different in brightness value, they exert a great influence on total images.
For example, in a case where a right image of FIG. 2B is more bright than a left image of FIG. 2A, if a stereo matching algorithm is performed on the right image of FIG. 2B and the left image of FIG. 2A, the image according to depth information can hardly be discriminated.